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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import pytorch_lightning as pl\n",
    "from functools import partial\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from model import MInterface\n",
    "from data import DInterface\n",
    "\n",
    "# from mlib import *"
   ]
  },
  {
   "source": [
    "> This notebook is an example for analyzing a kfold result. There is some mismatching with the main code since it's directly adapted from one of my project. Please refer to the code and modify accordingly."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_path = r'lightning_logs/version_XX/checkpoints/XXXX.ckpt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_module_drone = DInterface(\n",
    "                 dataset='standard_data',\n",
    "                 batch_size=1,\n",
    "                 data_dir=r'data/ref/val',\n",
    "                 class_num=10,\n",
    "                 train=False,\n",
    "                 no_augment=True,\n",
    "                 aug_prob=0.5,\n",
    "                 mean_sen=[0.485, 0.456, 0.406],\n",
    "                 std_sen=[0.229, 0.224, 0.225])\n",
    "\n",
    "data_module.setup()\n",
    "loader = data_module.val_dataloader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = MInterface.load_from_checkpoint(net_path).to(device)\n",
    "model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report\n",
    "import pandas as pd\n",
    "\n",
    "def plot_classification_report(trues, preds, names, valid_labels, \n",
    "                               title='Classification Report Plot', \n",
    "                               out_path=None, \n",
    "                               figsize=(14,10)):\n",
    "    report = classification_report(trues,\n",
    "                                preds,\n",
    "                                labels=valid_labels,\n",
    "                                target_names=names,\n",
    "                                output_dict=True)\n",
    "\n",
    "    f, ax = plt.subplots(figsize=figsize)\n",
    "    ax = sns.heatmap(pd.DataFrame(report).iloc[:-1, :].T, annot=True)\n",
    "    ax.set_title(title)\n",
    "    if out_path is not None:\n",
    "        plt.savefig(out_path)\n",
    "\n",
    "def plot_confusion_matrix(trues, preds, names, \n",
    "                          title=\"Confusion Matrix Visualization\", \n",
    "                          out_path=None, \n",
    "                          figsize=(10,7)):\n",
    "    cm = confusion_matrix(trues, preds)\n",
    "    df_cm = pd.DataFrame(cm, index = names,\n",
    "                    columns = names)\n",
    "    plt.figure(figsize=figsize)\n",
    "    ax=sns.heatmap(df_cm, annot=True)\n",
    "    ax.set_title(title)\n",
    "    if out_path is not None:\n",
    "        plt.savefig(out_path) #'confusion matrix of sr.png'\n",
    "\n",
    "def vis_metrix(trues, preds, names, \n",
    "               valid_labels, \n",
    "               explore_type='f1-score', \n",
    "               title=None, \n",
    "               figsize=(10,7)):\n",
    "    report = classification_report(trues,\n",
    "                                   preds,\n",
    "                                   labels=valid_labels,\n",
    "                                   target_names=names,\n",
    "                                   output_dict=True)\n",
    "    f1s = [report[name]['f1-score'] for name in target_names]\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    ax = sns.barplot(x=target_names, y=f1s, ax=ax)\n",
    "    if title is None:\n",
    "        title = f\"{explore_type} Bar Plot\"\n",
    "    else:\n",
    "        title = f\"{explore_type} {title}\"\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "def vis_melt_metrix(result_holder, names,\n",
    "               valid_labels, \n",
    "               explore_type='f1-score', \n",
    "               title=None, \n",
    "               figsize=(10,7)):\n",
    "    report_drone = classification_report(result_holder['trues'],\n",
    "                                    result_holder['drone'],\n",
    "                                    labels=valid_labels,\n",
    "                                    target_names=names,\n",
    "                                    output_dict=True)\n",
    "    report_sr = classification_report(result_holder['trues'],\n",
    "                                    result_holder['sr'],\n",
    "                                    labels=valid_labels,\n",
    "                                    target_names=names,\n",
    "                                    output_dict=True)\n",
    "\n",
    "    mcs_drone = [report_drone[name][explore_type] for name in target_names]\n",
    "    mcs_sr = [report_sr[name][explore_type] for name in target_names]\n",
    "\n",
    "    metrics_holder = {'class':target_names, 'Drone':mcs_drone, 'SR HSI': mcs_sr}\n",
    "\n",
    "    df = pd.DataFrame(metrics_holder)\n",
    "    df = pd.melt(df, id_vars=\"class\", var_name=\"source\", value_name=explore_type)\n",
    "    fig, ax = plt.subplots(figsize=(10,7))\n",
    "    sns.barplot(x='class', y=explore_type, hue='source', data=df, ax=ax)\n",
    "    if title is None:\n",
    "        title = f\"{explore_type} Bar Plot\"\n",
    "    else:\n",
    "        title = f\"{explore_type} {title}\"\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "def test_model(test_type='drone'):\n",
    "    labels_list = []\n",
    "    res_list = []\n",
    "    loader = loader_sr if test_type == 'sr' else loader_drone\n",
    "    model = model_sr if test_type == 'sr' else model_drone\n",
    "    for data in loader:\n",
    "        img, labels, filename = data\n",
    "        img = img.to(device)\n",
    "        out = model(img).cpu()\n",
    "        label_digit = labels.argmax(axis=1)\n",
    "        out_digit = out.argmax(axis=1)    \n",
    "        correct_num = sum(label_digit==out_digit).cpu().item()\n",
    "        labels_list.append(label_digit.item())\n",
    "        res_list.append(out_digit.item())\n",
    "        # break\n",
    "    return labels_list, res_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_holder = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "test_type = 'drone'\n",
    "type_str = 'Drone' if test_type == 'drone' else 'SR HSI'\n",
    "\n",
    "label_dict = mio.load('data/ref/9cls_crop_dict.pkl')\n",
    "rev_dict = {v:k for k,v in label_dict.items()}\n",
    "\n",
    "labels_list, res_list = test_model(test_type=test_type)\n",
    "result_holder['trues']=labels_list\n",
    "result_holder[test_type]=res_list\n",
    "\n",
    "valid_labels = list(set(labels_list)|set(res_list))\n",
    "target_names = [rev_dict[i] for i in valid_labels]\n",
    "\n",
    "plot_classification_report(labels_list, res_list, target_names, valid_labels,#label_dict, \n",
    "                           title=f\"{type_str} Crop Classification Results\", \n",
    "                           out_path=f'results/{test_type}_result_report.png')\n",
    "plot_confusion_matrix(labels_list, res_list, target_names, \n",
    "                      title=f\"{type_str} Crop Classification Results\", \n",
    "                      out_path=f'results/{test_type}_confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "vis_melt_metrix(result_holder, target_names,\n",
    "            valid_labels, \n",
    "            explore_type='f1-score', \n",
    "            title=None, \n",
    "            figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         9\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.17      0.22      0.19        36\n",
      "           4       0.36      0.43      0.39        23\n",
      "           5       0.00      0.00      0.00         1\n",
      "           7       0.00      0.00      0.00         9\n",
      "           9       0.00      0.00      0.00         1\n",
      "          10       0.42      0.60      0.49        62\n",
      "          14       0.00      0.00      0.00        22\n",
      "          17       0.00      0.00      0.00         4\n",
      "          18       0.00      0.00      0.00         7\n",
      "          19       0.00      0.00      0.00         2\n",
      "          20       0.00      0.00      0.00         1\n",
      "          22       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.31       180\n",
      "   macro avg       0.06      0.08      0.07       180\n",
      "weighted avg       0.22      0.31      0.26       180\n",
      "\n",
      "/home/miracle/anaconda3/envs/geo/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/miracle/anaconda3/envs/geo/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/miracle/anaconda3/envs/geo/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "txt_report = classification_report(labels_list, drone_res_list)\n",
    "print(txt_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "OrderedDict([(4, 60), (1, 37), (2, 31), (5, 17), (3, 10), (0, 10), (8, 3), (6, 2), (7, 1)])\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter, OrderedDict\n",
    "counts = Counter(labels_list)\n",
    "counts_ordered = OrderedDict(sorted(counts.items(), key=lambda kv: kv[1], reverse=True))\n",
    "print(counts_ordered)"
   ]
  },
  {
   "source": [
    "# KFold"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold=5\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model_fold(loader, model, test_type):\n",
    "    labels_list = []\n",
    "    res_list = []\n",
    "    for data in loader:\n",
    "        img, labels, filename = data\n",
    "        img = img.to(device)\n",
    "        out = model(img).cpu()\n",
    "        label_digit = labels.argmax(axis=1)\n",
    "        out_digit = out.argmax(axis=1)    \n",
    "        correct_num = sum(label_digit==out_digit).cpu().item()\n",
    "        labels_list.append(label_digit.item())\n",
    "        res_list.append(out_digit.item())\n",
    "    return labels_list, res_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kfold_test(test_type='sr'):\n",
    "    means = [0.485, 0.456, 0.406]\n",
    "    stds  = [0.229, 0.224, 0.225]\n",
    "    log_root = f'kfold_log/{test_type}'\n",
    "    type_str = 'Drone' if test_type == 'drone' else 'SR HSI'\n",
    "    model_paths = [glob.glob(op.join(log_root, f'version_{i}', 'checkpoints', 'best*'))[0] for i in range(5)]\n",
    "\n",
    "    labels_list_total = []\n",
    "    res_list_total = []\n",
    "    for fold_idx in range(5):\n",
    "        data_module = DInterface(\n",
    "                        dataset='standard_data',\n",
    "                        batch_size=1,\n",
    "                        data_dir=r'data/ref/val',\n",
    "                        class_num=10,\n",
    "                        train=False,\n",
    "                        no_augment=True,\n",
    "                        aug_prob=0.5,\n",
    "                        mean_sen=means,\n",
    "                        std_sen=stds,\n",
    "                        kfold=5,\n",
    "                        fold_num=fold_idx)\n",
    "        \n",
    "        data_module.setup()\n",
    "        loader = data_module.val_dataloader()\n",
    "        model = MInterface.load_from_checkpoint(model_paths[fold_idx]).to(device)\n",
    "        model = model.eval()\n",
    "        labels_list, res_list = test_model_fold(loader, model, test_type)\n",
    "        labels_list_total.extend(labels_list)\n",
    "        res_list_total.extend(res_list)\n",
    "    return labels_list_total, res_list_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_holder = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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I7bp1qtCmaceWRNOiXNX3djJrZzLipU8Y/f5XNGnXjGUFhZz98JW03rkdcybO4P1/vcKalasTfRoioajXvGFZEgLx95+2la6fJh1bEkmLclnf28ioncU3Lw1m7PtfVWiz23H7MKH/yITELKnHa8CclVSurHwN7O3uewB9iSchM4FngEfdvZu7r73iWwL7A8cC9wfrngcuAjCz+sSrMYOCbfsAF7j7IdUd3MwaAScBu7r7bsDdVbTpY2ZjzGzMmGXTtuhk5Q/kXmExkhalZZcOvHXRw7xx3v0ccM1JNOrQgsx62XQ+ojuP738dj/a6ivSsDLqetF9IQYsknlkVKytfP9Eobbt25NmLHuDp8+/jyKtPpmmHlkSiUdp06cA3rw/joT/dwpqVqznsyhMSE7hIMqjiAvIqrp/WXTvw8kUP8eL593PI1SfRpEOLsu3R9Cg7H9adiYO+2+rhiiSrVE5W2gBDzGwicBOw6wbafujuMXf/GWgO4O4jgE5m1gw4C3jP3UuC9sPcfdFGjl8IrAKeN7OTgfXGN7j7s+7ew9179KjTab0OZOsrmr+I+i0bly3Xa9mIogVLKrbJXcRvI8ZTvHI1KxcvY/aoyTTfuR0d9u/CkjkLWbGoiFhJKZMHj6ZN9x0SfAYi4VkyfxENWq27fhq0bMTSvIpDIZfOL+CXEeNZs3I1yxcX8duoybTeuR1L5hewZP4iZo2Lf1EzbtD3tOnSPpHhi4SqcP4i6req+P5TWMX1M3XEBIpXrmbF4iJmjPqFFjtvV7Z9x97dyJk0g2X56w8PEwHic1YS9QhJKicrTwBPuntX4HLiw7aqU37cQfmvOl4jPnzsIuClcuuXl3teQsXXKRMgSGx6Ae8BJwK6RUcSmjd+Oo06tKBB26ZE0qPsetzeTB32Q4U2U4b9QLtenbFohLTMWrTutj3503IozCmg9R6dSMusBUCH/XYlf1pOGKchEorZ43+jafsWNGrTlGh6lD2P25dJla6fiUPHsH3PnYhEI6Rn1mK7bp1YMG0eRQuXsiSngGYdWwKw435dmF/uxhYi27q543+jSfsWNAyun92P24dfKl0/Pw/9gfY9O5ddP227dWJhuQn4ux+/L+MHaAiY1GwpO2cFqA+svaIvKLe+CKi3fvMqvQyMAua7+0/VtJkF7GJmGcQTlUOBr82sDpDt7oPM7DtA47ySkJfG+OT2lznn1b9j0Qjj3h7Bwl/n0f2cQwH44Y3PyJ+Ww7QRE7hiyP14LMbYvsNZOHUuAL8MGkWfj+8hVlrK/J9m8eObn4d5OiIJFSuN8d7tL3Hlq7fGb1389hfM/3Uu+51zGADfvPEpC37L4ZcR4/j74AfxmDOy3+fkBtfPe3e8xHmPXUVaehr5c/J488ZnwjwdkYSKlcbof/vLXPzqzVg0wpi3h5P36zx6Be8/o974jIW/5TB1xASuGXw/HnPG9PuCBcH1k55Zix3278IHtz4f5mlIsqsBc1as8vjJZGRmMaD8V9qPAL8BjxJPWL4Derp7bzPbEXgXiBGfYH8J5W5dXPk2yGY2mPgwsWeC5QuBHu5+Vbk2DwInAL8Ca4D+wBDgI+IJjAEPu/sr1Z3Dndudk/wvtEiSKrDSsEMQSVnZKT2IQiR89818s6oZfElh9dSvE/b5MmPH/UN5HVIiWdlazCwbmAjs6e5Lt+axlKyIbD4lKyKbT8mKyJZJ6mRl8ojEJSs7HRTK61Bj/w9mZocBk4EntnaiIiIiIiIiv18qz1nZIu7+KdAu7DhERERERDZLDZizUmMrKyIiIiIiktyUrIiIiIiISFKqscPARERERERSWog/1pgoqqyIiIiIiEhSUmVFRERERCQVaYK9iIiIiIhIOFRZERERERFJRZqzIiIiIiIiEg5VVkREREREUpB7adghbHWqrIiIiIiISFJSZUVEREREJBXpbmAiIiIiIiLhUGVFRERERCQV6W5gIiIiIiIi4VBlJUHOqZsfdggiKauwMDPsEERSVkZ6SdghiMjWojkrIiIiIiIi4VBlRUREREQkFcX0OysiIiIiIiKhULIiIiIiIiJJScPARERERERSkSbYi4iIiIiIhEOVFRERERGRVKQfhRQREREREQmHKisiIiIiIqlIc1ZERERERETCocqKiIiIiEgq0pwVERERERGRcKiyIiIiIiKSilRZERERERERCYcqKyIiIiIiKci9NOwQtjpVVkREREREJCmpsiIiIiIikoo0Z0VERERERCQcqqzINi9rvx40ufkKLBql8L1PWPLC2xW2N7joVOr86RAALBolvWNbZh5wBrHCIuqfeyL1TjkazCh89xOWvv5BGKcgEpq6B+1Bmzsuw6IRCvoOY8F/36uwvdnlJ9HwxAMBsLQomZ3aMLHb+ZQuXUa0Xm3aPngVWTu2A3dm3fQEK36cEsZpiISi9oHdafGPPlg0wuJ+Qyn43zsVtje+7GTqH39wfCEtQsb2bZnS82xiS5fR6KITaXD6EYCzesoscv72KL6mOPEnIcmtBvyCfejJipk58Lq7nxcspwG5wPfufuwffKwLgR7uftUf2a8ksUiEprf9hZzLbqFkfj5t+j3B8i++o3j67LImS156lyUvvQtA9kF70eD8k4kVFlGr03bUO+Vo5p51DV5cTMtn7mXFl99TPDsnrLMRSaxIhLZ3X860c/5JcW4BnQc8zNJho1j165yyJnn/+4C8/8WT+HqH9aTZJcdTunQZAK3vuJSi4T8y84oHsPQ0IlkZoZyGSCgiEVrecSWzLriN4vn5dPzgUYo++44109ZdPwXPvU/Bc+8DUOeQXjS++ERiS5eR1rwxjS44jt+OvBJfvYbWj99MveMOYul7n4Z1NiKhSYZhYMuBLmaWFSwfDswLMR7ZhmR07Uzx7BxK5s6HkhKWfTKc2ofsU237OsccTNGg4QCkd2zHqgm/4KtWQ2mMVWMmUPvQ/RIUuUj4srvtwOqZ81kzewFeXMLiAV9R/4he1bZvePwBLO7/JQCROlnU6bUrBX2HAeDFJZQWLk9I3CLJIGv3HVkzK4fiOfOhuISlA7+k7mF7V9u+/nEHsXTAiLJlS4timbUgGiGSlUHJgoJEhC2SdJIhWQH4BPhT8Pws4K21G8zsDjO7sdzyJDNrHzx+MbPnzOwnMxu6NuExs55mNsHMRprZQ2Y2qdyx2prZYDObYmb/LNfvuWY2yszGmdn/zCwarH/azMYEx/hXufYzzexfZvajmU00s522zksjWyKtWWNK5i8sWy5ZkE9asyZVtrXMDLL378HyYV8DsGbaTDK7dyVSv2582wE9SWvRNCFxiySDWi0asyYnv2x5TW4B6c0bV9nWMmtRr/eeLBk0EoCMdi0oWbSUdv++hs6DHqXtA1epsiI1SlrzxhTnrrt+Subnb+D6yaDOgd0pHPxNvO2CAgqef58dv3qZHUe+TqxoOcu/HpuQuCXFxGKJe4QkWZKVvsCZZpYJ7AZ8v4n77QA85e67AkuAU4L1LwFXuPs+QOUbUPcCzgG6AaeZWQ8z2xk4A9jP3bsF+5wTtP8/d+8RxHWQme1Wrq98d98TeBq4kUrMrE+Q6Izpu2juJp6S/KHM1l/nXmXT7N57s2rsT8QKiwAonj6HJS++Tavn7qPlM/eweuoMvHTbv5+5SJkqLp/qrp/6h/di+ZhfyoaAkRYlu8v25L82mCnH/JXYylU0//MpVe4rsk2q6v2nGnUP7cWKH34mFlw/kXp1qHvY3vza+2Km7nselp1J/RMO3lqRiiS1pEhW3H0C0J54VWXQ79h1hruPC57/ALQ3swZAXXf/Nlj/ZqV9hrl7gbuvBN4H9gcOBboDo81sXLDcMWh/upn9CIwFdgV2KdfX++WPXcV5PevuPdy9x5mN2vyO05I/SsmC/ArVkLTmTShZWHUpvc7RB7EsGAK2VtH7Q5h7+lXkXHgjsaVFFM/SCEWpOdbkFlCr1bpKZK2WjSnOW1Rl24bHHcDij74qWy7OzWdNbj4rxk0FYMmgb8nqsv3WDVgkiZTMzye95brrJ61FE4qrGcpV79gDKwwBq71fN9bMWUDpokIoKaVoyLdk7bnzVo9ZUpDHEvcISVIkK4H+wMOUGwIWKKFinJnlnq8u97yU+A0DNvZVRuWvBT3Y5xV37xY8Orv7HWbWgXjF5FB33w34uJrjrz22JJnVk6aQ3q41aa2bQ1oadY7uzfIvvluvXaRONlk9dmP5F99WWB9tVB+AtBZNqX3ofiz7ZHgiwhZJCivG/0pGh5bUatsMS0+j4XEHsHTYqPXaRepmU2fvXVk6dF1RvGThEopz88no2BqAuvvtVmFivsi2buWEqdRq35r0Ns0hPY36xx7Iss/WHzgSqZNN7V5dKfp03XtTSc5Csrp1xjLjQydr77s7q3/T9SM1UzJ9wH4RWOruE82sd7n1M4FjAcxsT6DDhjpx98VmVmRme7v7d8CZlZocbmaNgJXAicDFwArgIzN71N3zgu11gXrEbwCw1MyaA0cDw7fkJCXBSmPk3/sULf93LxaNUPjBUIp/m0W90+NTpArf/hiA2ofux4pvf8BXrq6we/NHbyfaoC5eUkr+PU8SK1yW8FMQCU1pjLn/eJbtX7sjfuvifp+xauocGp97FAAFrw8GoMGRe1P05Thila6fubc/R/vHr8fS01g9ez6zb3w84acgEprSGPP/9TTtXr4Li0RY8u4wVv86m4ZnHQ3A4rc+AaDukfuy7OsfK7z/rBw/haLB39Cx/3/w0lJW/TSdJX0/CeU0JMnVgB+FNK9m/HHCAjBb5u51Kq3rDdzo7scGk+Y/ApoBo4kP2zo6aDrQ3bsE+9wI1AkqInsBzxFPNIYDB7r7fsGti48BagOdgDfd/V/B/mcAtxCv4hQDf3H378zsZWAvYDrxSkp/d3/ZzGYSvw1yvpn1AB52997VnedvXY4M94UWSWGFhZkbbyQiVcpILwk7BJGUtstvH2/6BKQEWzn0vwn7fJl1xJ9DeR1CT1a2BjOr4+7Lguc3Ay3d/dowY1KyIrL5lKyIbD4lKyJbJqmTlSFPJi5ZOfKqUF6HZBoG9kf6k5ndQvz8ZgEXhhuOiIiIiIj8XttksuLu/YB+YcchIiIiIrLV1IA5K8l0NzAREREREZEy22RlRURERERkm6fKioiIiIiISDhUWRERERERSUUh/rJ8oqiyIiIiIiIiSUmVFRERERGRVKQ5KyIiIiIiIuFQsiIiIiIiIklJw8BERERERFKRJtiLiIiIiIiEQ5UVEREREZFUpAn2IiIiIiIi4VBlRUREREQkFWnOioiIiIiISDhUWUmQVk+eEXYIIimr+ZCPww5BJGVFO+0QdggisrVozoqIiIiIiEg4VFkREREREUlFqqyIiIiIiIiEQ5UVEREREZFU5B52BFudKisiIiIiIpKUVFkREREREUlFmrMiIiIiIiISDlVWRERERERSkSorIiIiIiIi4VBlRUREREQkFbkqKyIiIiIiIqFQsiIiIiIiIklJw8BERERERFKRJtiLiIiIiIiEQ5UVEREREZFU5B52BFudKisiIiIiIpKUVFkREREREUlFmrMiIiIiIiISDlVWZJv3zaTpPPj2Z8RiMU7af3cuPmrvCtsLl6/in68OYu7CJdRKT+Nf5x9Np9ZNy7aXxmKcfe8rNGtQlyeuOjXR4YuEKrpjNzKOvRgiEYpHf0bxiA8qNsjIJvOMa7EGTSASpfirjyj54Yv4tsxsMk/+M5Hm7QBn1XtPEZs9NeHnIJIsvpmex4OfTSLmzkm7tePivXeosL1w1Rr++cl45i5ZTq1olH8dvTudmtYLKVpJCaqspDYzczN7rdxympktNLOBG9mvh5k9vvUjlK2tNBbjvreG8dTVp/H+HZcyePTP/JaTX6HN85+MpHObZrxz+8XcfdGfeLDfZxW2v/nZGDq0aJzIsEWSg0XIOP4yVr50DysevY603ffHmrWp0CR9n6OI5c1h5eM3sPK528k45gKIxr8HyzjuYkqmjmXFo9ew4vEbiOXNDeMsRJJCacy579OJPHXaXrx/ycEM/iWH3/KLKrR5fuQ0OjerxzsX9ebuP3Xjwc9+CidYkSSyTScrwHKgi5llBcuHA/M2tpO7j3H3a7ZqZJIQk2bk0rZZA9o0bUB6WpQje+zM8PG/VmgzPTefvXZuD0CHFo3JKVhKQeFyABYsLuSridM5ef/dEx26SOgibTsRK5iPL14ApSWUjP+atJ17VmzkjmXE/xdrtTLxlcsgVgoZWUTb70LJmCD5Ly2BVSsSfAYiyWNS7mLaNqhNmwa1SY9GOHLnVgyfNr9Cm+kFRey1XRMAOjSuS07hCgqWrw4jXEkVHkvcYxOY2VFmNsXMppnZzVVsb2hmH5jZBDMbZWZdNtbntp6sAHwC/Cl4fhbw1toNZtbLzL41s7HBn52D9b3XVl/MbJCZjQseS83sAjOLmtlDZjY6eLEvT/hZySbJW1JEi4brSujNG9Ylb8myCm12bNOMz36cAsDEGTnkLlrKgsXxb7seevszrjulN2aWuKBFkoTVa4QvXVeJ9MJFWP2KVcbikZ9gTduQfcvzZF/7CKsHvAjuRBo1x5cXknHqVWRd/RAZJ18J6RmJPgWRpJG3bBUt6maVLTevm0le0aoKbXZsVo/PpsYTmIm5i8ldupIFRSsTGqfI5jKzKPAUcDSwC3CWme1SqdmtwDh33w04H/jPxvqtCclKX+BMM8sEdgO+L7dtMnCgu+8B3A7cW3lndz/G3bsBlwCzgA+D50vdvSfQE7jMzDpU3tfM+pjZGDMb88KAEX/sWckmqeru45XTjouP2pvCFas4/a6X6PvFj3Ru25xoJMKXE6bRsG5tdtmuRSJCFUlCVSTple7pH92xG7HcGay471JWPHEjGcdfChlZEIkSadWR4u+HsPKJm/A1q6nV+6QExS2SfKr6OYzK34NdvFcnClcVc/rLI+j7www6N69HNKIvy6R6HvOEPTZBL2Cau0939zXEP4OfUKnNLsBnAO4+GWhvZs031Ok2P8He3SeYWXviVZVBlTbXB14xsx2If65Nr6oPM2sCvAac7u5LzewIYDczO7VcPzsAMyod+1ngWYCVw1/c9n+1Jwk1b1CX+YsLy5YXLC6iaYM6FdrUycrgzgvjxTd355j/e4bWTeozZMwvjBj/K19P+o01xaUsX7maW18YwL2XHJfQcxAJixcWYPWblC1bvUZ44aIKbdK7H8KaYNK9F8wntjiPSNPW+JJ8vLCA2Jz4sMuSSSOpdZCSFam5mtfNZH65KsmColU0rZNZoU2djHTuPKYbELwf/e8zWtfPTmSYItUysz5An3Krng0+667VGphTbnkusFelbsYDJwNfm1kvYDugDbCguuNu88lKoD/wMNAbKD+G4S7gC3c/KUhohlfeMShp9QXudPdJa1cDV7v7kK0Ys/wBdm3fktl5i5mXv4RmDeoyZMwv6yUbhStWkVUrnfS0KO9/PZ7uO7SlTlYG15x0ENecdBAAo6fM5tVho5SoSI0SmzuNSJOWWMNmeOEi0nbfn9V9H6vQxpfkk7Z9V9bM/AWrU59Ik1bEFi2AFUX4knysSSs8P4e07btqgr3UaLu2bMDsxcuZt2QFzepmMuSXHO49bs8KbQpXFZOVHiU9GuH9CbPp3rYxdTKq/B5VJC6BdwMr/yV8NaoqA1b+sv5+4D9mNg6YCIwFSjZ03JqSrLxIfNjWRDPrXW59fdZNuL+wmn3vBya4e99y64YAV5rZ5+5ebGY7AvPcffkfG7ZsqbRohJvPPJwr//M2sZhzwn5d6dSqKe+MGAvAaQftwYzcAm57+WOiZnRs2YQ7zj865KhFkkQsxur+z5N18T/AIhSP+ZxY3hzSeh0BQMmooaz5/B0yTruKrGsfAYw1g1+HFfE5X6sHvEDmGddCNB1ftIBV7z4Z4smIhCstEuHmw7pw5TvfEXPnhK5t6dSkLu+MnQnAaXu0Z0ZBEbd9PI5oBDo2rssdR+vmLpJS5gJtyy23AXLKN3D3QuAiAItPCJ5BpZFJlZlXNYhyG2Fmy9y9TqV1vYEb3f1YM9sHeAVYCHwOnOfu7Su1ceAn1mV9twMDgbuB44hnkQuBE919aXWxaBiYyOYrHfJx2CGIpKxop/WmVIrI75B1ycNJO3FoxdNXJ+zzZfaVT2zwdTCzNGAqcCjxYsBo4Gx3/6lcmwbACndfY2aXAQe4+/kb6nebrqxUTlSCdcMJhnu5+0hgx3Kb/1FFm+r+Ym4NHiIiIiIiNZq7l5jZVcRHIEWBF939JzO7Itj+DLAz8KqZlQI/E79p1QZt08mKiIiIiIgkhrsPotINrYIkZe3zkcRvSrXJlKyIiIiIiKSiTbulcEqrCb+zIiIiIiIiKUiVFRERERGRVJTAWxeHRZUVERERERFJSqqsiIiIiIikIlVWREREREREwqHKioiIiIhIKtqGf9x9LVVWREREREQkKamyIiIiIiKSijRnRUREREREJByqrIiIiIiIpCL9gr2IiIiIiEg4VFkREREREUlFrjkrIiIiIiIioVBlRUREREQkFWnOioiIiIiISDhUWUmQSecMCDsEERGpkX4LOwCRlNbzkrAjqNmUrIiIiIiIpCDXj0KKiIiIiIiEQ5UVEREREZFUpAn2IiIiIiIi4VBlRUREREQkFelHIUVERERERMKhyoqIiIiISCrSnBUREREREZFwqLIiIiIiIpKK9DsrIiIiIiIi4VBlRUREREQkFWnOioiIiIiISDhUWRERERERSUX6nRUREREREZFwqLIiIiIiIpKKNGdFREREREQkHEpWREREREQkKWkYmIiIiIhICvIa8KOQSlZkm1ev9x60u/MSLBJh4VufMv+p9ytsb3HFiTQ++cD4QjRK1g6tGbvbhZQuWUa0XjbtH/4LWZ3bgcOMG55k+Q9TQjgLkXDo+hHZfLp+RLZcjU5WzKwUmEj8dZgBnOfuS8ysFfC4u58aaoCy5SIRtrunD1PPuoM1uQXsMuhBlgwdxapf55Y1mf/Mh8x/5kMA6h/egxaXHU/pkmUAtLvzUpZ+MZbf+jyEpacRyaoVxlmIhEPXj8jm0/UjiaAJ9tu8le7ezd27AIuAvwC4e44SlW1D7T12YPXMXFbPXoAXl7Doo69peGSvats3PuEACj78CoBInSzq7rUL+W99CoAXl1BauCIhcYskA10/IptP14/IH6OmJyvljQRaA5hZezObFDyPmtnDZjbRzCaY2dXB+u5mNsLMfjCzIWbWMsTYpRq1WjRiTU5+2fKa3ALSWzSusm0ksxb1e+/B4kEjAcjYrjnFBYV0ePRqdhnyb9o/9GciWRkJiVskGej6Edl8un4kIWKeuEdIlKwQT0iAQ4H+VWzuA3QA9nD33YA3zCwdeAI41d27Ay8C91TRbx8zG2NmYz5YPnOrxS8bYLb+Oq/6gmtwRE+WjZlcVoK3aJTaXTuS9+pgfj7yBmIrVtPyqpO3ZrQiyUXXj8jm0/Uj8oeo6clKlpmNAwqARsCwKtocBjzj7iUA7r4I6Ax0AYYF+98GtKm8o7s/6+493L3HSbXbb5UTkA1bk1tArVZNypZrtWxM8YJFVbZtdPz+ZSX4tfuuyS1g+dhfAVj08bdkd+24dQMWSSK6fkQ2n64fSQiPJe4RkpqerKx0927AdkAtgjkrlRhQ+asQA34K5rt0c/eu7n7E1g1VNsfycb+S0aEltdo2w9LTaHTC/iweOnq9dtG62dTde1eWDBlVtq5k4RLW5OSTuX0rAOrtvxsrp85db1+RbZWuH5HNp+tH5I9Ro+8Gtpa7LzWza4CPzOzpSpuHAleY2XB3LzGzRsAUoKmZ7ePuI4NhYTu6+0+Jjl02ojTG7Nueo/Ob/4RIhPx+n7Fq6hyannckAAtfGwJAg6P3YumX44itXF1h91n/eI6OT/wVS09j9ewFzLj+iYSfgkhodP2IbD5dP5IINeBuYObVjJ+sCcxsmbvXKbc8AHgb+AoY6O5dzCwNeBA4CigGnnP3J82sG/A4UJ940veYuz9X3bFGtz6p5r7QIiIiIimq57wPqpiAlByWXX98wj5f1nmkfyivQ42urJRPVILl48otdgnWlQDXB4/ybccBB27lEEVEREREquQ1oLJS0+esiIiIiIhIkqrRlRURERERkZSlyoqIiIiIiEg4VFkREREREUlFsfB+/yRRVFkREREREZGkpGRFRERERESSkoaBiYiIiIikIk2wFxERERERCYcqKyIiIiIiqUiVFRERERERkXCosiIiIiIikoLcVVkREREREREJhSorIiIiIiKpSHNWREREREREwqHKioiIiIhIKlJlRUREREREJByqrCTI9r0WhR2CSMrKOu/IsEMQSVmxCRPDDkFEthJXZUVERERERCQcqqyIiIiIiKQiVVZERERERETCocqKiIiIiEgqioUdwNanyoqIiIiIiCQlJSsiIiIiIpKUNAxMRERERCQF6dbFIiIiIiIiIVFlRUREREQkFamyIiIiIiIiEg5VVkREREREUpFuXSwiIiIiIhIOVVZERERERFKQ7gYmIiIiIiISElVWRERERERSkeasiIiIiIiIhEOVFRERERGRFKQ5KyIiIiIiIiFRZUW2eel79CL7kqshEmH1px+z6v03K2y37NrUvu42Ik2aQTTKqo/6sebzTyC9FvXueRzS0iEapXjkCFb2fSmksxAJxzdT5vHgwFHEYs5JPXfg4t5dK2wvXLmaf777DXMXLaNWWoR/nbIfnVo0ZP6S5dz2ztcUFK3EDE7ptSPn7LdLSGchEo5op92pddT5EIlQ8uMXFH/dv2KDjCwyTv4LVr8JFolS/O1ASsaNwBq3JOO0a8qaRRo2Y80X71Ly3ScJPgNJejVgzso2mayYWSkwsdyqE919ZjVtl7l7HTNrDwx09y4JCFESJRIhu891FN1xA7GChdR78H+sGfUNsbmzyppkHH0SpXNmsuzeW7B69an/5Ous+XIYFK+h8Pa/wqqVEI1S794nWfPj95RO/TnEExJJnNJYjPv6f8czlxxB83rZnPPUxxy0c1u2b96grM3zX0ykc8tGPHreIczIW8p9/b/j2UuPJBoxbjimBzu3bszy1cWc9cRA9u7UqsK+Its0M2odcxGrXrsXLywg87J7KJnyA75wXlmT9F5HEFs4j+K3HobsumRf/QglE7/GC3JZ9cwtZf1k3fBfSn8ZHdKJiIRrWx0GttLdu5V7zAw7IAlH2g47E8udR2xBLpSUsObrz6nVa/+KjdyxrGwALDMLX1YIpaXxbatWxv+MpsUfvu2PDRVZa9KcfNo2rkebRnVJT4ty5O4dGP7LnAptpuctYa9OLQHo0Kw+OYuXUVC0kqb1stm5dWMAamek07FZffIKVyT8HETCEmndidii+fjiPCgtpXTSSNI696jYyMEysgCwWpn4ymUQq/hVebRjF3zRAnxpfqJClxTiscQ9wrKtJisVmFkdM/vMzH40s4lmdsJG2mea2UtB27FmdnCwfpCZ7RY8H2tmtwfP7zKzS7f+mcjvZY2aUJqfV7YcK1hIpHGTCm1WDXqfaJvtaPDC+9R/7CVWvPDEuqQkEqHeI8/T8OUPKR4/htJff0lk+CKhyitcQYv6tcuWm9fLJm/p8gptdmzZiM8mzQZg4pyF5C5ZzoJKScm8xcuYnLOIrm0rXnsi2zKr1xAvLChb9sICrF7DCm2KRw0h0qQVWTf8l6w/P8iaT15d70uxaJd9KZn0bUJiFklG22qykmVm44LHB8Aq4CR33xM4GPi3mdkG9v8LgLt3Bc4CXjGzTOBL4AAzqweUAPsF7fcHvqrciZn1MbMxZjbmlZm5f9jJye9Q1V9zpeJI+h69KJ3xK0suOZml119K9mXXQVBpIRaj8PpLWXLpaaTtsDPRdh22esgiyaKqOmLl/3VefFAXClet5vTH+9P328l0btmIaGRdmxWri7nx9S+46die1MmstZUjFkkmG3//iXbajdj8Waz8959Z+czN1DrmQggqLfEGUdI6d6fkp++3aqQiyWybnLNCMAxs7YKZpQP3mtmBxKcitQaaA/Or2X9/4AkAd59sZrOAHYknJNcAM4CPgcPNLBto7+5TKnfi7s8CzwIsOukgjR8KgRcsJNqkWdlypHFTYosqltIzDjm6bNJ9bP48Ynm5RNu0o/TXyev6WbGM4klj44nN7BmJCV4kZM3rZTO/XCVlQeEKmtbLrtCmTmYt7jw1PrTS3Tnmwfdo3bAOAMWlMW54YzjHdOvIoV22S1zgIknACxdh9RqXLVu9xnjR4gpt0rr1pvjrj+LtFy3Alywk0qQVsXm/ARDt1I1Y7gxYvjRxgUtqqQET7LfVykpl5wBNge5BErMAyNxA++qqLqOBHsABxKssY4HLgB/+sEjlD1Xy62QiLdsQadYC0tKotf8hFI/+pkKbWH4e6bvtCYDVb0i0VVti83OxevWx7PiHLmrVIn33HpTOm53oUxAJza5tmjA7v5B5i4ooLillyPgZHLRzmwptCleuobgkPsfr/dG/0r1Dc+pk1sLd+dd739ChaX3OO2DXMMIXCVUs5zcijVtgDZpCNEq0yz6UTKn4ccGX5hPtGNzXp3Z9rHFLYovXDV1O67ovJRM1BExqtm21slJZfSDP3YuD+Scb+4rvS+IJzudmtiPQDpji7mvMbA5wOnAX8QTo4eAhyShWyornHqPuPx+O37r4s0GUzplJxpHHA7B6SH9Wvv0Kda65hXqPvQQGK177H160lOh2Hal9za0QiUDEWPPNcIrHjAz5hEQSJy0a4ebj9+LKFz8l5jFO6LEDnZo35J3v44Xk0/bqzIy8Jdz2ztdEI0bHZg2445R9ARg3K4+BY6ezQ4uGnP54/HatVx+xJwfs1Kba44lsU2Ix1gx6mczzbgGLUDJ2OL5wLmk9DgOgZMynFH/5ARknXkHalQ+AGWs+fQtWFMX3T69FtGNXVg94PsSTkGQX5sT3RDHfBu9utPZ2xOWWmwADgHRgHPG5Jke7+8yqbl0czE95BuhOfG7K9e7+RdDXXcCh7r6vmbUC5hGv2Py4oZg0DExk82Wdd2TYIYikrNiEiRtvJCLVqn3HWxua5xyq/KMT9/myyScjQnkdtsnKSvlEJVjOB/bZUNvg9sZdguergAuraf8P4B/B8xyqHzImIiIiIrL11IDKSk2ZsyIiIiIiIilmm6ysiIiIiIhs62rCnBVVVkREREREJCmpsiIiIiIikoJUWREREREREQmJKisiIiIiIilIlRUREREREZGQqLIiIiIiIpKKfNv/uT9VVkREREREJCmpsiIiIiIikoI0Z0VERERERCQkSlZERERERCQpaRiYiIiIiEgK8pgm2IuIiIiIiIRCyYqIiIiISAryWOIem8LMjjKzKWY2zcxurmJ7fTMbYGbjzewnM7toY30qWRERERERkS1iZlHgKeBoYBfgLDPbpVKzvwA/u/vuQG/g32ZWa0P9as5Kgpw5JivsEERS1ucfPxV2CCIpq23dJmGHIJLSZtwRdgTV8+T6UchewDR3nw5gZn2BE4Cfy7VxoK6ZGVAHWASUbKhTVVZERERERGSDzKyPmY0p9+hTqUlrYE655bnBuvKeBHYGcoCJwLXuGx5kpsqKiIiIiEgKSuSPQrr7s8CzG2hSVZnHKy0fCYwDDgG2B4aZ2VfuXlhdp6qsiIiIiIjIlpoLtC233IZ4BaW8i4D3PW4aMAPYaUOdqrIiIiIiIpKCkux3VkYDO5hZB2AecCZwdqU2s4FDga/MrDnQGZi+oU6VrIiIiIiIyBZx9xIzuwoYAkSBF939JzO7Itj+DHAX8LKZTSQ+bOzv7p6/oX6VrIiIiIiIpCCvPCMkZO4+CBhUad0z5Z7nAEf8nj41Z0VERERERJKSKisiIiIiIikoyeasbBWqrIiIiIiISFJSZUVEREREJAWpsiIiIiIiIhISJSsiIiIiIpKUNAxMRERERCQFJduti7cGVVZERERERCQpqbIiIiIiIpKCNMFeREREREQkJKqsiIiIiIikIHdVVkREREREREKhZEW2eT16d+eF4c/z0lcvcsafT6+yzW5778bTg5/i2U//x8PvPFi2/vqH/8rbY/vy7KfPJCpckaRy5BG9+WnSl0z++Wv+dtNf1tt+0IH7ULDwF8aMHsqY0UO57f+uA6BNm1Z8OvQdJk4Yzvhxn3P1VZckOHKR8B14yL589v1HfDF6AFdce/F62/farwfjZ3zNx8P78fHwflx94+Vl2+rWq8t/X3qYT7/7kGEjP2CPHrslMnRJER5L3CMsGgYGmNkyd6+zge3XAc+6+4rERSV/hEgkwlV3/4Wbz76V/Nx8nhj4OCOHfcfsX2eXtaldrzZX3/MXbj3vNhbmLKRB4/pl24a9M4z+Lw/gb4/dGEb4IqGKRCI8/p97OOqYs5g7N5fvRg5iwMCh/PLLrxXaff31KE446YIK60pKSrjpb/9i7LhJ1KlTm1HfD+bTz75cb1+RbVUkEuHOB2/lvFMuZ37OAj769E0+HTycaVOmV2g3euRYLj376vX2/+d9f2PEZ9/w54tuJD09jcysrESFLpJUVFnZNNcB2WEHIb9f526dyZmZy/zZ8ykpLmFE/xHse8Q+FdoccuLBfDP4WxbmLARgScHSsm0Tv59E0ZKihMYskix69dyD336byYwZsykuLubttz/i+OOO3KR958/PY+y4SQAsW7acyZN/pXWrFlszXJGksvueXZg1Yw5zZs2juLiEAR8M5vCje2/SvnXq1qbXPt3p9/oHABQXl1BUqPciWV/MLWGPsChZCZhZbzMbWG75STO70MyuAVoBX5jZF8G2I8xspJn9aGbvmFm1VRkJV5MWjcuSEICFufk0btG4QpvWHVpTp34dHnr7QZ76+AkOO+XQRIcpkpRatW7BnLk5Zctz5+XSqoqEY++9u/PDmGEM7P8au+yy43rbt9uuDd1278L3o8Zu1XhFkkmLls3InTe/bHl+Th4tWjZfr92ePXdj0Ii3eanfU+zQeXsA2m7XhkUFi3noyTsZ+EU/7n/sn2Rlq7IiNZOSlY1w98eBHOBgdz/YzJoAtwGHufuewBjg+qr2NbM+ZjbGzMbMXTYncUHLOrb+NwFe6edeo2lRdujaiX9c8A9uOff/OOfas2ndoXWiIhRJWrYJ18+PYyfSsVMvuvc4nKf++xLvvfNihe21a2fzdr/nuP7Gf1JUtGyrxiuSTDbl+vlpwi/s3+0ojjnodF557i3+99qjAKSlRdl1t51446V3OPbgM1ixYiVXVjHnRcTdEvYIi5KV329vYBfgGzMbB1wAbFdVQ3d/1t17uHuPNnXaJjBEWSs/N5+mrZqWLTdt2YRFCxat12bM8B9YtXI1hYsLmfj9JDru0jHRoYoknXlzc2nbplXZcpvWLcnNXVChTVHRMpYvj0/n+2Tw56Snp9G4cUMA0tLSeKffc7z11gd8+OEniQtcJAnk5iygZet1lcgWrZqxYH5ehTbLipazYvlKAIZ/+jXp6Wk0bNSA3JwFzM9ZwLgfJgLwSf9h7LrbTokLXiSJKFlZp4SKr0dmNe0MGObu3YLHLu6u29wkqSnjp9C6fStatG1OWnoaBx1/ECOHfVehzbdDR9Kl165EohEyMjPYaY/OzJk2u5oeRWqO0WPG0alTB9q3b0t6ejqnn34CAwYOrdCmefN1Xwb07NGNSCRCQcFiAJ579t/8Mnkaj/3n2YTGLZIMJoz9ifYd29GmXWvS09M47qSj+PSTERXaNGm2bljy7nt2wSIRFi9aQn5eAbnzFtCxU/y70H0P3Gu9ifkiEP8F+0Q9wqK7ga0zC9jFzDKIJyqHAl8H24qAukA+8B3wlJl1cvdpZpYNtHH3qWEELRsWK43x5D/+y72v30MkGmFIv6HMmjqLP517DAAfvz6IOdPmMGb4D/xv6NO4O5+8NZiZU2YBcMuTN7Pb3rtRv1E93hj1Gq/9+3UG9xsS5imJJExpaSnXXncbgz5+k2gkwsuv9OPnn6fS57LzAHj2udc45eQ/cfnl51NSUsqqlas459w/A7Dfvj0579xTmTDxZ8aMjic4//jH/Xwy+PPQzkckkUpLS/nn3+/j1XeeJhKN8M6bH/LrlN84+8LTAHjz5Xc45vjDOeei0yktKWHVqtVcc+nfy/b/58338+j/7qNWejqzZ83lpqtuD+tUREJllcdP1jRmlgYscPfGZvYgcALwK7AG6O/uL5vZ1cBfgNxg3sohwANARtDNbe7ef0PHOaLtUTX7hRbZAp8vmBh2CCIpq23dJmGHIJLSZhSMT9qfif9lh2MS9vly518HhfI6qLICuwK/Abj734C/VW7g7k8AT5Rb/hzomagARURERERqoho9Z8XMrgDeIn53LxERERERSSI1urLi7s8Az4Qdh4iIiIjI7xXmxPdEqdGVFRERERERSV41urIiIiIiIpKqYiH+WGOiqLIiIiIiIiJJSZUVEREREZEU5KqsiIiIiIiIhEOVFRERERGRFFQTfttdlRUREREREUlKqqyIiIiIiKQg3Q1MREREREQkJKqsiIiIiIikIN0NTEREREREJCSqrIiIiIiIpCDdDUxERERERCQkqqyIiIiIiKQg3Q1MREREREQkJKqsJEi7aJ2wQxBJWWmRaNghiKSsBul6/xHZVuluYCIiIiIiIiFRsiIiIiIiIklJw8BERERERFKQJtiLiIiIiIiERJUVEREREZEUVAN+E1KVFRERERERSU6qrIiIiIiIpCDNWREREREREQmJKisiIiIiIilIPwopIiIiIiISElVWRERERERSUCzsABJAlRUREREREUlKqqyIiIiIiKQgR3NWREREREREQqHKioiIiIhICorVgJ+wV2VFRERERESSkiorIiIiIiIpKKY5KyIiIiIiIuFQsiLbvF0P6sbdn/2He4c/wdFXnlhlm85778rtgx7iX0Mf5aZ+/6qwzSIRbv/4Ia5+4ZYERCuSXA4//CAmTPiCn376khtv/PN62w88cG8WLJjE999/wvfff8Ktt14LQEZGBl991Z9Rowbz44+f8o9/XJ/o0EVCt+/Be/HB12/x0ch+XHTVuett777vHnw5dQh9P32Zvp++TJ/rLyrbdk6fM3h3xOu8M/w17nv6Dmpl1Epk6CJJY6PDwMysBfAY0BNYDcwErnP3qb/nQGbWHhjo7l1+d5RbwMx6AOe7+zV/YJ93AMvc/eE/qk/ZOiwS4Zw7L+WRc+9k8fxF3Nb/fsYNG0PutLllbbLqZXPOXZfy2AX3sCgnn7qN61Xo47CLjiF32lwy62QnOnyRUEUiEf7zn7v505/OYe7cXL75ZgADBw5j8uRfK7T75pvRnHzyRRXWrV69mqOOOpPly1eQlpbG55+/x5AhXzBq1NhEnoJIaCKRCDffdwNXnn4dC3LzeGPw84wY+jXTp86s0G7s9+O59ry/VVjXtEUTzrr0VE458BxWr1rDA8/eyZEnHsaAfoMSeAaSCmr8rYvNzIAPgOHuvr277wLcCjRPRHB/BHcf80cmKpJaOnTrRN6s+eTPyaO0uIRRA76h2xE9K7TZ6/gD+HHw9yzKyQegqKCwbFvDFo3Y7ZDufNX3s4TGLZIMevbsxm+/zWTGjNkUFxfzzjsDOO64IzZ5/+XLVwCQnp5Genoa7jXgtjUigS577MycGXOZNzuHkuIShnz4Gb2PPGCT949Go2RkZhCNRsnMymTh/PytGK1I8trYMLCDgWJ3f2btCncf5+5fAZjZTWY22swmmNm/gnV3mdm1a9ub2T1mViFZMLNMM3vJzCaa2VgzOzhYf6GZvW9mg83sVzN7sKqgzOx+M/s5OO7DwbqXzewZM/vKzKaa2bHB+t5mNjB4foeZvWhmw81sevm4zOxDM/vBzH4ysz7l1h9lZj+a2XgzK/+JdZeq+pHk0rB5IxbnrPsf/OLcAho2b1ShTfOOLcmuX4eb+v6Lfwx4gH1OPqhs2xm3X8S7972mD1lSI7Vq1YK5c3PKlufNy6VVq/W/q9prrz0ZNWowH330CjvvvGPZ+kgkwvfff8KcOWP57LOvGT16XCLCFkkKzVo2ZUFOXtnygtw8mrZsul673bp3od9nL/Pkmw/TsXMHABbOz+fVp9/ikx/eZ9iEj1hWuJzvRoxKWOySOmIJfIRlY8PAugA/VLXBzI4AdgB6AQb0N7MDgReA94H/mFkEODNoU7fc7n8BcPeuZrYTMNTM1r7DdQP2ID7kbIqZPeHuc8odtxFwErCTu7uZNSjXb3vgIGB74Asz61RF6DsRT8LqBv0/7e7FwMXuvsjMsoDRZvYe8WTuOeBAd58RHHtj/ZR/jfoAfQD2a7QHO9XtWNVLKVuTrV8erZx4RKNRtuvakX+f/S9qZdbilvfvZfrYqTTv0IqigqXMmjSdznvvmqiIRZKGbcL1M3bsJHbccR+WL1/BkUcezDvvPEeXLvGEPxaLsddeR1O/fj3efvtZdtllR37++XeNIBZJXVVcP1S6fiZPmMIxPU5h5YqV7H/oPjz60n2csO+Z1K1fl95HHcCxvU6jaGkRDz53N8eccgSD3huaoOBFkseWTLA/IniMBX4k/uF9B3efCRSY2R5rt7t7QaV99wdeA3D3ycAsYG2y8pm7L3X3VcDPwHaV9i0EVgHPm9nJwIpy295295i7/wpMD2Kq7GN3X+3u+UAe64a0XWNm44HvgLbEE7G9gS/dfUYQ66JN6KeMuz/r7j3cvYcSlXAsnl9Aw1ZNypYbtmzMkrzF67X5acQ41qxczbLFRUwd9TNtdm5Ppx6d2f2wntz/9X/p88R17LRvFy59VEU0qTnmzculTZtWZcutW7ckNzevQpuiomVlw72GDPmC9PQ0GjduWKHN0qWFfPnldxxxRO+tHrNIssjLyaN5q2Zly81bNltvKNfyZStYuWIlAF9/NpK09DQaNKrPXgf2IGd2DosLllBSUsrng0awe8+uCY1fUoNjCXuEZWPJyk9A92q2GXCfu3cLHp3c/YVg2/PAhcBFwIvV7Fud1eWel1Kp+uPuJcQrNe8BJwKDy2+u1FdVY3fW69/MegOHAfu4++7EE7DMIM7qxv9sME5JDjPHT6N5+5Y0adOMaHoavY7bj/HDRldoM27oaHbouTORaIRambXo2G0HcqfN5f0H3+Rv+1zOzfv/mWevfozJ307i+b8+HtKZiCTemDHj6dSpA+3btyU9PZ3TTjuOgQOHVWjTvPm6YS09euxOJBKhoGAxTZo0on79+M0qMjMzOOSQ/Zky5beExi8Spp/GTaZdxza0ateStPQ0jjzxUIYP/bpCm8ZN1w3Y2HWPnTEzlixayvy5C+javQuZWRkA9DqgBzN+nZXQ+EWSxcY+YH8O3Gtml7n7cwBm1hPIBoYAd5nZG+6+zMxaE5/fkkd8Uv6dQDpwdhX9fgmcA3weDP9qB0wB9txYwGZWB8h290Fm9h0wrdzm08zsFaAD0DHoc++N9QnUBxa7+4pgWNrafUYCT5lZh7XDwCpVVyTJxUpjvHn781z36m1EohG+eftzcn6dy0HnxCcJj3hjKLm/zWPSiLHcMfjfeMz5qt9n5Eyds5GeRbZ9paWlXHfdPxgw4DWi0SivvNKPX36ZyqWXxm/B+vzzr3PSScfQp895lJSUsHLlKs477yoAWrRoxvPPP0I0GiUSifDeewP55BPdqEJqjtLSUh649VH++9YjRKJRPnprINOnzODU808E4N1XP+Sw4w7mtAtOorSkhFWr1nDLFf8EYNLYn/l04Be8OfQlSktLmTxxKu+99lGIZyPJKsy5JIliG5s4bGatiN+6uDvx4Vczid+6+NdgIv2lQdNlwLnu/luw3zPAEne/OVhuT3DrYjPLBJ4J+iwBrnf3L8zsQqCHu18V7DMQeNjdh5eLpyXwEesqHw+7+ytm9jKwGOhBfEjW9e4+MKia3Ojux1a+5bCZTQKOBXKBD4HWxBOcpsAd7j7czI4G7iVehcpz98Or6ycYAlelS9ufqhnaIpvp9fmaWCqyuXZu0DbsEERS2tj53yTt/YEHNz8zYZ8vj1rQN5TXYaPJymZ1Gp9Y/yNwWjB/ZKsLkpWB7v5uIo73eylZEdl8SlZENp+SFZEtk8zJyqAEJivHhJSs/OG/YG9muxAfmvVZohIVERERERHZ9vzhk8Ld/Wfi80USyt0vTPQxRURERETCUuN/wV5ERERERCQsut2uiIiIiEgKim37hRVVVkREREREJDmpsiIiIiIikoJimrMiIiIiIiISDiUrIiIiIiKSlDQMTEREREQkBdWEXxxXZUVERERERJKSKisiIiIiIikoFnYACaDKioiIiIiIJCVVVkREREREUlDMdOtiERERERGRUKiyIiIiIiKSgnQ3MBERERERkZCospIgj528JuwQRFLW4+2ODjsEkZTlhcvCDkFEthLdDUxERERERCQkqqyIiIiIiKSg2LZ/MzBVVkREREREJDmpsiIiIiIikoJibPulFVVWREREREQkKamyIiIiIiKSgvQ7KyIiIiIiIiFRsiIiIiIiIklJw8BERERERFKQbl0sIiIiIiISElVWRERERERSUCzsABJAlRUREREREUlKqqyIiIiIiKQg3bpYREREREQkJKqsiIiIiIikIN0NTEREREREJCSqrIiIiIiIpCDdDUxERERERCQkqqzINi+6055knHgpRKIUfzeU4s/fq9ggM5vMc67HGjaNt/niA0pGfxZsq03mGVcRabEd4Kzq+zixWVMSfg4iYYlstyu1DjodIhFKJn1NyZghFRvUyiTjqEuwug3j188Pwyj9+VsA0rodQlqX/cEsvu/Yz0I4A5HwRLffjVpHnhe/fsYOp/ibARUbZGSRcdKfsXqNsUiU4pEfUzL+S6xxSzJOubqsWaRhM9YMf5eS7wcn+Awk2dWEykpKJytm1hhY++7XAigFFgbLvdx9zR90nAbA2e7+3z+iP0kgi5Bx8uWsfOZ2fGkBWX/9NyU/jcIXzClrkr7fn4gtmMOaF+6G2vWofcvTlPw4AkpLyDjpMkom/0jJKw9ANA3SM0I8GZEEM6PWwWex+v3H8GWLyTzrFkqnT8AX5ZY1Sdv9YGKLcinu/xRk1SHrgjtZOfl7rGFz0rrsz6q+90FpKRknXUPpjIn4krwQT0gkgcyodfSFrHr9PrxwEZmX3kXJlB/x/HllTdJ7Hk5s4TyK+/4bsuuS/ZeHKZn4DV6Qy6pnby3rJ+uvT1I6eUxIJyKy6czsKOA/QBR43t3vr7T9JuCcYDEN2Blo6u6LquszpYeBuXuBu3dz927AM8Cja5f/qEQl0AD48x/YnyRIpN0OxPJz8UULoLSEkrFfkdZlr0qtHMvIAsAysvAVyyBWChlZRDvuSsn3w+LNSktg1fLEnoBIiCItOuBL8/DCfIiVUjJ1DNHtd6/UysuSeEvPwFcth1iMSKMWxObPgJJi8Bilc6cS3b5bws9BJCyR1tsTW7wAX7IQYqWU/vQdaZ27V2zkYLUygfifvnIZxCp+Vx7t0AVfnIcvzU9U6JJC3BL32BgziwJPAUcDuwBnmdkuFeJ1f6jcZ/dbgBEbSlQgxZOVqpjZoWY21swmmtmLZpYRrJ9pZvea2UgzG2Nme5rZEDP7zcyuCNrUMbPPzOzHYP8Tgm7vB7Y3s3Fm9pDFPWRmk4J2Z4R1vrJhVr8xvmTd/+B9ST5Wv3GFNsVff4w1b0P2HS+TfdPjrP7gOXAn0rgFvnwpGWdeS9b1j5Fx+lVQS5UVqTmsdgO8aHHZshctxmo3qNCmZNwXRBq1JOuyB8k893bWDO8HOLH8HCKtd4DM2pCWTrRDV6xuo8SegEiIrG4jfGlB2bIXLooPlyynePRQIk1bk/XXJ8m64n7WDHmNyj/zF911b0omfZuIkEW2VC9gmrtPD4oGfYETNtD+LOCtjXW6rSUrmcDLwBnu3pV4eenKctvnuPs+wFdBu1OBvYE7g+2rgJPcfU/gYODfZmbAzcBvQSZ4E3Ay0A3YHTgMeMjMWlYOxsz6BInRmBcnzPqjz1U2hVXxVYBXeiPovAexeTNYcceFrPj3dWScfDlkZEEkSqT19hR/+wkrH7kOX7OKWoecmqDARZLAJnyTFt1uV2IL57Dyub+x6o27qXXwWVArE188n+IxQ8g8+ToyTryW2MI58YqlSI1W6f1n+92IzZ/FykevYuX/bqXWURdArax1DSJR0jp3p+Tn7xMcp6SKWAIf5T/XBo8+lcJpDcwptzw3WLceM8sGjgLeq2p7edtashIFZrj71GD5FeDActv7B39OBL539yJ3XwisCualGHCvmU0APiX+Ajev4jj7A2+5e6m7LwBGAD0rN3L3Z929h7v3uHi37f6A05Pfy5fkYw2alC1bgyZ4YcVqY3qvQymZMDLePj+X2KIFRJq3wZfm40vzic2O/3MqGf8tkTYdExe8SMh82ZIK3wRb3Yb48iUV2qTtui+l08bG2y9diBfmE2nYAoDSn75h1Zv3sPrdh2HVCs1XkRrFixZVqORbvUZ40ZIKbdK6HUjJ5NHx9sGQsUiTdd99Rjt1I5Y7E5YXJiJkkQ0q/7k2eDxbqUlVX3F5FesAjgO+2dgQMNj2kpWNTShYHfwZK/d87XIa8Qk/TYHuwVi6BcSrNZXVgN8L3TbE5vxKpGkrrFFziKaRtscBlE6q+A2VL84nbcf4OHyr04BIs9bECubjRUviyU7T+JcCaTvuTmzBnPWOIbKtis2fiTVohtVrHP+Gd8celP42vmKbokVE2+0UX8iuizVsTmxpcJ+TrLpAPMmJdtqDkimjExm+SKhi86YTadQCaxC/02R0170pmfpDhTa+tIBoh13jC7XrYY1bElu8LqlP67KPhoBJKpkLtC233AbIqabtmWzCEDBI8buBVSETaG9mndx9GnAe8arHpqoP5Ll7sZkdDKwthxQBdcu1+xK43MxeARoRr97ctMXRyx8vFmP1+/8jq88dEIlQPOpTYgvmkLbPUQCUjBzMmmH9yDjrWrJuehww1gx8BZYXAbD6/WfJPPd6iKbjBfNZ1fc/4Z2LSKJ5jDVf9CXjpGvBIpT89A2+KJe0rvGCdcnELyn5/mNqHXEhmefeDkDx1x+U3Ygi49jLsczaECtlzRdvweoVoZ2KSMJ5jDWfvEzmOX+PXz/jRuAL55HW/VAASn74jOIvPyDjhCtIu/x+MFjzWV9YuSy+f1otoh27sPrjF0I8CUl2SXbr4tHADmbWAZhHPCE5u3IjM6sPHAScuymdmnt11ZnUYmZ3AMuAscDDxBOx0cCV7r7azGYCPdw938wuDJ5fFew7E+gRdDUASAfGAfsBR7v7TDN7E9gN+AT4G/Ag8bsdOHC3u/fbUHzLrj9+23ihRUIQabfelDAR2UReuCzsEERSWu3b30jaETVPtj03YZ8vr5rz+kZfBzM7BniM+NSMF939nrU3snL3Z4I2FwJHufuZm3LcbSZZSXZKVkQ2n5IVkc2nZEVkyyRzsvJEApOVqzchWdkatrU5KyIiIiIiso3Y1uasiIiIiIjUCLGkrfn8cVRZERERERGRpKTKioiIiIhICkqyu4FtFaqsiIiIiIhIUlJlRUREREQkBamyIiIiIiIiEhJVVkREREREUlBN+BE/VVZERERERCQpqbIiIiIiIpKC9DsrIiIiIiIiIVFlRUREREQkBeluYCIiIiIiIiFRsiIiIiIiIklJw8BERERERFKQbl0sIiIiIiISElVWEiTj1sfCDkEkZe3V9fywQxBJWffG2oQdgkhKO+r2sCOoXqwG1FZUWRERERERkaSkyoqIiIiISArSrYtFRERERERCosqKiIiIiEgK2vZnrKiyIiIiIiIiSUqVFRERERGRFKQ5KyIiIiIiIiFRZUVEREREJAXFLOwItj5VVkREREREJCmpsiIiIiIikoL0C/YiIiIiIiIhUWVFRERERCQFbft1FVVWREREREQkSSlZERERERGRpKRhYCIiIiIiKUg/CikiIiIiIhISVVZERERERFKQbl0sIiIiIiISElVWpEa77d5H+PKbUTRq2IAPX38m7HBEks6+B+/FjXdeSzQa4YM3B/Lyk69X2N59nz145OX7yJmdC8Dng0bw3KMvA3BOn9M58ezjcHem/TKdO/56L2tWr0n0KYiEpsnBu7Pz3RdANMLcNz5nxhP9K2xv/+djaXXK/gBYWpQ6O7Tm810uo3jJ8niDiLHv0HtZNX8xP577YKLDlxSw7ddVUrSyYmYtzKyvmf1mZj+b2SAz2zHsuCT1nHjM4TzzyN1hhyGSlCKRCH+/93quPudGTjnoXI468TA67Nh+vXbjvh/PWYdfxFmHX1SWqDRt0YQzLzmVc4+6hNMPPp9INMKRJxya2BMQCVPE2OX+ixlz9v18fcANtDxpP2rv2LpCk5n/Hci3h97Mt4fezNR73mLRyJ/XJSpA+8uOZtmvOYmOXCSppFyyYmYGfAAMd/ft3X0X4FagebiRSSrq0a0r9evVDTsMkaTUZY+dmTtzLvNm51BSXMKQjz6l95H7b/L+0WiUjMwMotEoWVkZLFyQvxWjFUkuDfbsxIoZ81k5Kw8vLmX+h9/S/Kge1bZvedJ+5H7wbdlyRstGND18T+a+8XkiwpUUFUvgIywpl6wABwPF7l42ZsfdxwFfm9lDZjbJzCaa2RkAZvZfMzs+eP6Bmb0YPL/EzO42s/ZmNtnMXjGzCWb2rpllB226m9kIM/vBzIaYWctg/WVmNtrMxpvZe2vbi4hsS5q2aMr8eXlly3m5C2nWoul67bp270LfT1/miTcepuOOHQBYOD+f157py6Ax7zF0/IcUFS3nuxGjExa7SNgyWjRiZU5B2fKqnEVktGhUZdtIVi2aHLw7CwZ+X7Zu57suYMqdb0CsJgz0EaleKiYrXYAfqlh/MtAN2B04DHgoSC6+BA4I2rQGdgme7w98FTzvDDzr7rsBhcCfzSwdeAI41d27Ay8C9wTt33f3nu6+O/ALcElVgZpZHzMbY2Zjnn/1rc09XxGRUMQL2RW5V/zgNHniFP7U81TOPOxC+r7wLo+8dC8AdevXpfeR+3PsXqdzZLcTycrO5JhTjkhI3CJJYf3Lh+pmGDQ7ojtLRk8pGwLW9PA9WZO/lMIJM7ZefLJNiOEJe4QlFZOV6uwPvOXupe6+ABgB9CSekBxgZrsAPwMLgiRmH2BtvXWOu38TPH896Ksz8cRomJmNA24D2gRtupjZV2Y2ETgH2LWqgNz9WXfv4e49Lj3/rD/4dEVEtq683DxatG5WttysZdP1hnItX7aClStWAvDN59+Rlp5Gg0b12euAHsybncuSgiWUlJTy+aAv2a1H14TGLxKm1bmLyGrVuGw5s1UjVs9fXGXblifuU2EIWMNeO9LsyO4cNPoJdv/fNTTeb1d2e+ovWz1mkWSUisnKT0D3KtZX/R2G+zygIXAU8SrLV8DpwDJ3L1rbrPJuQX8/uXu34NHV3dd+LfgycJW7dwX+BWRuwfmIiCSln8ZNpm2HtrRq25K09DSOPOEwRgz5pkKbxk3XDWvZtdvOWCTCkkVLmT9vAV2770pmVgYAvfbvzoxfZyYyfJFQLR37G9kdW5DVrimWHqXFifuSN2T9gSFpdbNouM8u5A0eU7Zu6j19Gb7HXxjR82rGX/44Bd/8xIS/PJXI8CVFeAIfYUnFWxd/DtxrZpe5+3MAZtYTWAycYWavAI2AA4Gbgn1GAtcBhwCNgXeDx1rtzGwfdx8JnAV8DUwBmq5dHwwL29HdfwLqArnBunOAeVv1jGWruemf9zN67ASWLCnk0BPP5c+XnMcpxx0ZdlgiSaG0tJQHbn2Ep956hEg0Qv++HzN96gxOOf8EAN579SMOO7Y3p15wEqUlpaxetZpbrvgnAJPG/sxnA7/gjaEvUlpSypRJU3n/9f4bOpzINsVLY/x8y0v06HsrFo0w960vWDZlLm3PPwyAOa9+CkDzY3pRMGICpStWhxmuSNKyyuOPU4GZtQIeI15hWQXMJJ6M9AGOJp4A3u3u/YL2lwB3uXurIMFYApzn7u+bWXtgEPGqy77Ar8G2FWbWDXgcqE88sXvM3Z8zsyuBvwGzgIlAXXe/cEMxF+dPT70XWiRJ7NX1/LBDEElZ98babLyRiFTrqAV9qxy9kwyubX9mwj5f/mdmOK9DKlZWcPcc4kO5KruJddWU8u1fAF4InhcDtSs1ibn7FVXsN454haby+qeBp3934CIiIiIisslSMlkREREREanpvAb8hn2NT1bcfSbxu36JiIiIiEgSScW7gYmIiIiISA1Q4ysrIiIiIiKpKBZ2AAmgyoqIiIiIiCQlVVZERERERFJQrAZMsFdlRUREREREkpIqKyIiIiIiKWjbr6uosiIiIiIiIklKlRURERERkRSkOSsiIiIiIiIhUWVFRERERCQF6XdWREREREREQqLKioiIiIhICnLNWREREREREQmHKisiIiIiIimoJsxZUbKSIHnHXRp2CCIpa0D7WmGHIJLC8sIOQERksylZERERERFJQZqzIiIiIiIiEhIlKyIiIiIikpQ0DExEREREJAXVhAn2qqyIiIiIiEhSUmVFRERERCQFxVwT7EVEREREREKhyoqIiIiISAra9usqqqyIiIiIiEiSUmVFRERERCQFxWpAbUWVFRERERERSUqqrIiIiIiIpCBXZUVERERERCQcqqyIiIiIiKQg/YK9iIiIiIhISFRZERERERFJQbobmIiIiIiISEhUWRERERERSUG6G5iIiIiIiEhIVFmRGiVj757Uv+4qLBphef9BLHvtrQrbrXZtGt5xK2nNm0E0yrI332bFx4NDilYkuej6EdkyuoZEfj8lK1JzRCI0uOFa8q+9idK8hTR78WlWffUtJTNnlTWpfeoJlMyYyaKb/o9Ig/o07/cKK4Z8CiUlIQYukgR0/YhsGV1DshXo1sU1hJkpaasBau2yEyVz51GakwslJaz49HMyD9y3YiN3LDsbAMvKIlZYBKWlIUQrklx0/YhsGV1DIpsn6ZMVM/vQzH4ws5/MrE+w7igz+9HMxpvZZ8G6Omb2kplNNLMJZnZKsH5Zub5ONbOXg+cvm9kjZvYF8ICZ9TKzb81sbPBn56Bd1MweLtfv1WZ2qJl9UK7fw83s/cS9KrI5Ik2bUJqXV7ZcmpdPtGnTCm2Wv/sh6e3b0WLAOzR7/QWWPPok+LY/eU1kY3T9iGwZXUOyNbh7wh5hSYWKwsXuvsjMsoDRZvYR8BxwoLvPMLNGQbt/AEvdvSuAmTXchL53BA5z91Izqxf0WWJmhwH3AqcAfYAOwB7BtkbAYuApM2vq7guBi4CXKnceJFd9AO7v0Jlzm7fa/FdBtpzZ+usqXXwZe/Wk+NffyL/qBqJtWtHkPw+RN+4yfMWKBAUpkqR0/YhsGV1DIpsl6SsrwDVmNh74DmhL/MP/l+4+A8DdFwXtDgOeWruTuy/ehL7fcfe19dX6wDtmNgl4FNi1XL/PuHvJ2uN5PL18DTjXzBoA+wCfVO7c3Z919x7u3kOJSvhieQuJNmtWthxt1oTS/PwKbWr/6ShWDv8KgNK5OZTmzCetfbuEximSjHT9iGwZXUOyNcTwhD3CktTJipn1Jp4s7OPuuwNjgfFQ5Stm1awvvy6z0rbl5Z7fBXzh7l2A48q1ra7fl4BzgbOIJz2a/Zbk1vwymbS2rYm2bAFpaWQfdgirvhpZoU3JgjwyeuwJQKRhQ9K2a0vpvJwwwhVJKrp+RLaMriGRzZPsw8DqA4vdfYWZ7QTsDWQAB5lZh7XDwILqylDgKuA6iA8DC6orC8xsZ2AKcBJQtIFjzQueX1hu/VDgCjMbvnYYWFBdyTGzHOA24PA/8qRlKymNseTfT9DksQcgEmX5wE8omTGT7JOOA2DFBwMoeuk1Gt72d5q9/jxgLH3qWWJLC8ONWyQZ6PoR2TK6hmQrqAl3A7MwJ8xsjJllAB8CrYknG02BO4As4nNKIkCeux9uZnWIDwPrDpQC/3L3983sVOABYA4wCajj7hcGE+0Huvu7wbH2AV4BFgKfA+e5e/vgTmEPAkcBxcBz7v5ksM+ZwHXuvvfGzmXePock7wstIiIiIlVqPfLzKiYcJYfj2h2bsM+XA2YPDOV1SOpkJdmZ2ZPAWHd/YWNtlayIiIiIpJ5kTlaObfenhH2+HDj741Beh2QfBpa0zOwH4nNebgg7FhERERGRbZGSlc3k7t3DjkFEREREaq4w79KVKEl9NzAREREREam5VFkREREREUlBNWHuuSorIiIiIiKSlFRZERERERFJQTXhd1ZUWRERERERkaSkyoqIiIiISApy3Q1MREREREQkHEpWREREREQkKWkYmIiIiIhICtKPQoqIiIiIiIRElRURERERkRSkH4UUEREREREJiZIVEREREZEUFMMT9tgUZnaUmU0xs2lmdnM1bXqb2Tgz+8nMRmysTw0DExERERGRLWJmUeAp4HBgLjDazPq7+8/l2jQA/gsc5e6zzazZxvpVspIga1ZEww5BJGWNydvo/8tEpBrNfU3YIYiktNZhB7ABSfajkL2Aae4+HcDM+gInAD+Xa3M28L67zwZw97yNdaphYCIiIiIiskFm1sfMxpR79KnUpDUwp9zyXNbP9XYEGprZcDP7wczO39hxVVkREREREUlBsQTeDczdnwWe3UATq2q3SstpQHfgUCALGGlm37n71Oo6VbIiIiIiIiJbai7QttxyGyCnijb57r4cWG5mXwK7A9UmKxoGJiIiIiKSgjyBj00wGtjBzDqYWS3gTKB/pTYfAQeYWZqZZQN7Ab9sqFNVVkREREREZIu4e4mZXQUMAaLAi+7+k5ldEWx/xt1/MbPBwAQgBjzv7pM21K+SFRERERGRFLSpv3+SKO4+CBhUad0zlZYfAh7a1D41DExERERERJKSKisiIiIiIiko2SorW4MqKyIiIiIikpSUrIiIiIiISFLSMDARERERkRTkCfxRyLCosiIiIiIiIklJlRURERERkRSkCfYiIiIiIiIhUWVFRERERCQFuSorIiIiIiIi4VBlRUREREQkBeluYCIiIiIiIiFRZUW2eVn79qDR3/+MRSIUffAJS1/sV2F7/QtOo/YxhwJgaRHSO7Rjdu/TiBUWUe/sk6h7ytFgRtF7gyh844MwTkEkNM0P3o097jwPi0aY/uZwpjw5oML2Ha/8E9udvB8Qv37q7dCaj7pcQVp2Br0ev5LMZvXxmDP99c+Z9vyQME5BJDQND+7G9nddhEUjzH/jM+Y8+WGF7W3+fDzNTj4AiF8/2Tu0YeSul1CyZBm9Rj9F6bJVeGkMLy1l7JE3h3AGkuxqwt3AlKzIti0SofGtVzP/8r9TsiCfVm8+yYrhIymePrusydJX3mHpK+8AkHXQ3tQ/92RihUWkd2pP3VOOJuecq/HiYlr89z5WfDWKktnzwjobkcSKGHveeyFfnnEfK3IXcdgnd5Ez9EeKpq67BqY+/TFTn/4YgJaH78GOfY6meMlyorXSGf+vN1gycSZptTM5bMjdLPhyUoV9RbZpkQid7ruEiaffxercRewx+D4Kho5hxdS5ZU3m/rc/c//bH4BGh3enzeXHUrJkWdn28afcQcmiooSHLpJMtvlhYGYWDTsGCU9Gl84Uz8mhZN58KClh+eDhZPfet9r2dY46mOWffAFAeod2rJ4wGV+1GkpjrPphArUP2S9RoYuErtEe27Ns5gKWz16IF5cy56PvaH1k92rbtztxX2Z/OBKAVXlLWDJxJgAly1dR+GsOWS0aJiJskaRQd49OrJwxn1Wz8/DiEhZ++A2Nj+xRbftmJ+1P3gdfJzBC2Ra4e8IeYQk1WTGz9mY22cyeN7NJZvaGmR1mZt+Y2a9m1ito18vMvjWzscGfnYP1UTN72MwmmtkEM7s6WD/TzG43s6+B08zsrKDNJDN7oJpYbjez0UGbZy1uZzMbVSneCcHzY4LYvzazx81s4FZ/weR3izZrQun8hWXLpXn5pDVvUmV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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "test_type = 'drone'\n",
    "type_str = 'Drone' if test_type == 'drone' else 'SR HSI'\n",
    "\n",
    "label_dict = mio.load('data/ref/9cls_crop_dict.pkl')\n",
    "rev_dict = {v:k for k,v in label_dict.items()}\n",
    "\n",
    "labels_list, res_list = kfold_test(test_type=test_type)\n",
    "result_holder['trues']=labels_list\n",
    "result_holder[test_type]=res_list\n",
    "\n",
    "valid_labels = list(set(labels_list)|set(res_list))\n",
    "target_names = [rev_dict[i] for i in valid_labels]\n",
    "\n",
    "plot_classification_report(labels_list, res_list, target_names, valid_labels,#label_dict, \n",
    "                           title=f\"results/{type_str} Crop Classification Results\", \n",
    "                           out_path=f'{test_type}_result_report.png')\n",
    "plot_confusion_matrix(labels_list, res_list, target_names, \n",
    "                      title=f\"{type_str} Crop Classification Results\", \n",
    "                      out_path=f'results/{test_type}_confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}